{"id":"W2305002039","doi":"10.1007/s11053-016-9296-1","title":"Optimizing Ore–Waste Dig-Limits as Part of Operational Mine Planning Through Genetic Algorithms","year":2016,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Limit (mathematics); Genetic algorithm; Dig; Computer science; Engineering; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004186477,0.0001445182,0.0002031073,0.0001575972,0.0001167551,0.00006373372,0.0003837944,0.0001328959,0.0001841632],"category_scores_gemma":[0.0001576631,0.0001059873,0.00005987661,0.0001987313,0.0001254678,0.0001868328,0.0001324742,0.0003630104,0.00004434252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001227868,"about_ca_system_score_gemma":0.00002852418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005115275,"about_ca_topic_score_gemma":0.000005656068,"domain_scores_codex":[0.9984774,0.00004938259,0.0003139219,0.0002484816,0.0004209425,0.0004898594],"domain_scores_gemma":[0.9991407,0.0003314188,0.00003117007,0.0002728889,0.0001375593,0.00008628167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007289101,0.0001996748,0.009899993,0.0007491242,0.0007891462,0.0002913104,0.02160654,0.1751495,0.3166216,0.00435918,0.143018,0.3265871],"study_design_scores_gemma":[0.002235551,0.0008547402,0.002485141,0.001464714,0.00002607863,0.0001115106,0.001861793,0.2746606,0.2623063,0.001759097,0.4509319,0.001302488],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874895,0.002911991,0.0009692975,0.0002846719,0.0001528109,0.000181501,0.00001911365,0.0001505512,0.007840523],"genre_scores_gemma":[0.9740347,0.0003411792,0.02266265,0.00002403651,0.0003871835,0.00003135244,0.000008522323,0.00004419471,0.002466152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3252846,"threshold_uncertainty_score":0.4322038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06836415481794421,"score_gpt":0.3398247887984149,"score_spread":0.2714606339804707,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}